Mental Health Sentiment Analysis API

Overview

The Mental Health Sentiment Analysis API is a RESTful API built with ASP.NET Core that classifies user-submitted statements into mental health categories (e.g. Normal, Anxiety, Depression, Stress, Suicidal, etc.). It uses ML.NET to process mental health data and train a multiclass classification model on the data. The API accepts a raw text statement via HTTP and returns the predicted mental health status.

I developed this to demonstrate end-to-end ML model training and deployment using C#, ML.NET and ASP.NET Core. This tool is for demo purposes only and should not be used for real world mental health assessments.

Features

• Text Cleaning: Custom mapping removes URLs, HTML tags, punctuation, digits, bracketed text, and normalizes to lowercase.
• TF-IDF Featurization: Tokenizes, removes stop-words, maps tokens to keys, then produces TF-IDF weighted n-grams.
• Hyperparameter Sweep: Search over varying L2 regularization strengths with 5-fold cross-validation to find the best multiclass classifier.
• ML Model: Trains and saves the classifier using ML.NET, which is loaded by the RESTful API developed in ASP.NET Core.
• Swagger Integration: Provides comprehensive API documentation and testing capabilities.

Prerequisites

• .NET 8.0
• A self-signed certificate to enable HTTPS (optional if using HTTP).
• An API client or Swagger UI (included with the project).

Model Performance

I performed hyperparameter tuning over SDCA, LBFGS, OVA + SDCA and OVA + LBFGS across varying L2-regularization strengths. The best cross-validated model was LBFGS with no L2 regularization.

Best Algorithm	Hyperparameters
LBFGS (Maximum Entropy)	No L2 Regularization

Metric	5-fold CV	Test Set
Micro Accuracy	0.7601	0.7580
Macro Accuracy	0.7087	0.7059
Log Loss	1.0966	1.1200

Summary

• Micro Accuracy (overall): 75.8% of all statements are classified correctly.

• Macro Accuracy (per-class average): 70.6% macro accuracy means the model's per-class performance nearly matches its overall score, so minority classes are not neglected.

• Log Loss: A log loss of 1.12 is reasonably low for a 7-class problem. It indicates the model is reasonably confident in its predictions.

• Model Generalization: The very small gap between cross-validated and test set scores shows the model generalizes well and is not over-fitting.

Setup and Running the API

Build and Run the Project

cd WebApi
dotnet build
dotnet run

The server will be hosted at https://localhost:5001 (or http://localhost:5000). You can test the API using tools like Postman, curl, or Swagger UI.

API Usage

Once the API is running, you can access the Swagger UI, which provides an interactive interface for testing the API endpoints. The Swagger UI is available at the following URLs:

https://localhost:5001/swagger
http://localhost:5000/swagger

The API will return a JSON object with:

• statement – the original text
• predictedStatus – the class with the highest score
• scores – confidence scores for each class
  [Normal, Anxiety, Depression, Stress, Suicidal, Bipolar, Personality Disorder].
  The seven values sum to 1.

Here is an example of how to call the API using curl and its output:

curl -X POST https://localhost:5001/api/MentalHealth/analyze \
     -H "Content-Type: application/json" \
     -d '"Nothing interests me lately. Getting out of bed feels pointless."'

{
  "statement":"Nothing interests me lately. Getting out of bed feels pointless.",
  "predictedStatus":"Depression",
  "scores":[0.0029239682,0.2543611,0.7271766,0.009098701,0.0048756558,0.0009974231,0.00056647585]
}

Architecture

Swagger UI